Perbandingan Pemodelan Hujan-Limpasan antara Artificial Neural Network (ANN) dan NRECA

Abstract

The analysis of water availability needs continuous and long discharge data. If the data is not long and continu- ous thus the rainfall-runoff model is needed. One of the available models, where parameters forming equation which have physical meaning illustrate ground water and surface runoff in the river, is NRECA (Non Recorded Catchments Area). Another model, an artificial neural network (ANN) is a computing system made up of a highly interconnected set of simple information processing elements, analogous to a neuron, called units. A neural network has an input layer, a hidden layer and an output layer. Each layer is made up of several nodes, and layers are interconnected by sets of correlation weights. The pattern of connectivity and the number of processing units in each layer may vary within some constraints. The input used is monthly rainfall whereas the output is monthly discharge. The difference between simulated discharge produced by ANN or NRECA and observed discharge is determined by mean absolute error, namely KAR (Kesalahan Absolut Rata-rata). For case study, 30 year monthly rainfall and discharge at Cikapundung-Gandok are used. For NRECA, the more error on KAR, the more deviation of discharge will be, particularly which is under average for dependable discharge as well as mean annual minimum discharge. It is concluded that in general NRECA is better but espe- cially for low flow ANN model is leading.